When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery
少即是多:二元反馈可能更有利于排序恢复
Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this paper challenges that conventional wisdom. We propose a general parametric framework for modeling ordinal paired comparisons without ties. The model adopts a generalized additive structure, featuring a link function that quantifies the preference difference…